The effectiveness of a search engine is measured by the relevance of the search results to the input user query. Search queries usually contain several words that define one or more concepts. Typically, some of the words in a search query are more relevant to defining the concepts than others. A search engine has no way of knowing which words in a search query are most relevant. As a result, search engines typically turn up many search results that are not relevant to the user's intent. Current measures of relevance include the similarity of the document's content to the given query and other metadata like the number of clicks a document receives for the given query. However, the click data is sparse and the number of unique documents clicked for a given query is small. The problem gets exacerbated for tail queries. So this provides little information about the relevance of documents not clicked for the given query.